Increased α1-3 fucosylation of α-1-acid glycoprotein (AGP) in pancreatic cancer

Balmaña, Meritxell; Giménez, Estela; Puerta, Angel de la; Llop, Esther; Figueras, Joan; Fort, Esther; Sanz‐Nebot, Victoria; Bolós, Carme de; Rizzi, Andreas; Barrabés, Sílvia; Frutos, Mercedes de; Peracaula, Rosa

doi:10.1016/j.jprot.2015.11.006

Cited by 52 publications

(53 citation statements)

References 38 publications

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“…1) 18 . We used IgG and AGP in human plasma as standard proteins to prove our concept, as IgG in human plasma is exclusively core-fucosylated, while AGP is mostly outer-fucosylated [43][44][45] . N-glycopeptide spectra identified from IgG and AGP using IQ-GPA software with <1% false discovery rate (FDR) were used to train and test the machine learning models.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry

Hwang

Jeong

Lee

et al. 2020

Sci Rep

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Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins such as core or outer isoforms remains a challenge. Here, we report for the first time the classification of N-glycopeptides as core-and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms such as the deep neural network (DNN) and support vector machine (SVM). Training and test sets of more than 800 MS/MS spectra of N-glycopeptides from the immunoglobulin gamma and alpha 1-acid-glycoprotein standards were selected for classification of the fucosylation types using supervised learning models. The bestperforming model had an accuracy of more than 99% against manual characterization and area under the curve values greater than 0.99, which were calculated by probability scores from target and decoy datasets. Finally, this model was applied to classify fucosylated N-glycoproteins from human plasma. A total of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified as the same type in both the DNN and SVM. Specifically, outer fucosylation was dominant in tri-and tetra-antennary N-glycopeptides, while core fucosylation was dominant in the mono-, bi-antennary and hybrid types of N-glycoproteins in human plasma. Thus, the machine learning methods can be combined with MS/MS to distinguish between different isoforms of fucosylated N-glycopeptides. Protein glycosylation is one of the most common post-translational modifications related to protein structure, stability, trafficking, and proteiN-protein interactions 1,2 Protein glycosylation is divided into O-or N-glycosylation according to the amino acid binding groups, which include the hydroxyl side chains of serine (S) or threonine (T) and the carboxy-amido nitrogen of asparagine (N) residues, respectively. The heterogeneity and complexity of N-glycosylation are due to the various combinations of four kinds of carbohydrate blocks, including N-acetylhexosamine (HexNAc; e.g., N-acetylglucosamine, N-acetylgalactosamine), hexose (Hex; e.g., glucose, galactose, mannose), fucose (Fuc), and sialic acid (Sia; N-acetylneuraminic acid). These combinations are made by their corresponding glycosyltransferases in the endoplasmic reticulum and Golgi apparatus 1. Various diseases, including cancer, involve the fucosylation of human N-glycosylation. This is due to two kinds of

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Section: Resultsmentioning

confidence: 99%

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry

Hwang

Jeong

Lee

et al. 2020

Sci Rep

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“…For example, pancreatic ductal adenocarcinomas are associated with increased α1,3fucosylation of α1-acid glycoprotein, which is of hepatic origin [102], while colorectal cancers are associated with altered core-fucosylation of IgG glycans, which are produced by plasma cells [103]. In particular, the core-fucosylation was increased in non-sialylated IgG glycans and decreased in those sialylated.…”

Section: Plasma Carriers Of Slex or Sleamentioning

confidence: 99%

Selectin Ligands Sialyl-Lewis a and Sialyl-Lewis x in Gastrointestinal Cancers

Trinchera

Aronica

Dall’Olio

2017

Biology

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The tetrasaccharide structures Siaα2,3Galβ1,3(Fucα1,4)GlcNAc and Siaα2,3Galβ1,4(Fucα1,3)GlcNAc constitute the epitopes of the carbohydrate antigens sialyl-Lewis a (sLea) and sialyl-Lewis x (sLex), respectively, and are the minimal requirement for selectin binding to their counter-receptors. Interaction of sLex expressed on the cell surface of leucocytes with E-selectin on endothelial cells allows their arrest and promotes their extravasation. Similarly, the rolling of cancer cells ectopically expressing the selectin ligands on endothelial cells is potentially a crucial step favoring the metastatic process. In this review, we focus on the biosynthetic steps giving rise to selectin ligand expression in cell lines and native tissues of gastrointestinal origin, trying to understand whether and how they are deregulated in cancer. We also discuss the use of such molecules in the diagnosis of gastrointestinal cancers, particularly in light of recent data questioning the ability of colon cancers to express sLea and the possible use of circulating sLex in the early detection of pancreatic cancer. Finally, we reviewed the data dealing with the mechanisms that link selectin ligand expression in gastrointestinal cells to cancer malignancy. This promising research field seems to require additional data on native patient tissues to reach more definitive conclusions.

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“…For instance, a change in the degree of branching for the carbohydrate chains on AGP has been used to detect infections in systemic lupus erythematosus (SLE) [5]. In addition, an increase in α1–3 fucosylation for AGP has been noted in pancreatic cancer [6], and hypersialylation of AGP has been reported in ovarian cancer and lymphoma [7,8]. …”

Section: Introductionmentioning

confidence: 99%

Glycoform analysis of alpha1-acid glycoprotein based on capillary electrophoresis and electrophoretic injection

Zhang

Clarke

et al. 2017

Journal of Chromatography A

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A method based on capillary electrophoresis (CE) with electrophoretic injection and absorbance detection was developed for the direct analysis of AGP glycoforms in human serum. Electrophoretic injection of AGP was performed in the reversed-polarity mode of CE with a capillary coated with poly(ethylene oxide) and that had minimal electroosmotic flow. This situation created an essentially stationary interface between the sample and running buffer during injection and sample stacking. This approach allowed an 11,000-fold increase in sample loading for a 5 min injection versus hydrodynamic injection and without introducing any significant levels of extra band-broadening. This method was used with sample pretreatment methods based on acid precipitation and desalting to examine AGP glycoforms in only 65 μL of serum. A limit of detection of 2.1–11.3 nM was obtained for the major AGP glycoform bands in serum, and the sample pretreatment method gave a recovery of 72.3–80.9% for these glycoforms. The precision for the migration times was ± 0.08–0.13% and the precision for the peak areas was ± 0.34–1.18% when using serum samples and an internal standard. This method was used for both normal pooled serum and serum from individuals with systemic lupus erythematosus. Results were obtained in a separation time of 25 min and allowed the comparison of up to eleven glycoform bands in these samples. A similar approach may be useful in examining additional glycoproteins in serum or other types of biological samples.

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Increased α1-3 fucosylation of α-1-acid glycoprotein (AGP) in pancreatic cancer

Cited by 52 publications

References 38 publications

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry

Selectin Ligands Sialyl-Lewis a and Sialyl-Lewis x in Gastrointestinal Cancers

Glycoform analysis of alpha1-acid glycoprotein based on capillary electrophoresis and electrophoretic injection

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